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Bio
Software Engineer specializing in Computer Vision, Deep Learning, and end-to-end system development. Experienced in ML model training, backend architecture, and building production-ready applications. Analytical, fast learner, and focused on delivering high-quality engineering solutions.
Skills
Python
PostgreSQL
MinIO
Docker
FastAPI
CUDA
PyTorch
PyQT6
Bootcamp Project
A complete classical + deep-learning denoising system for SEM images
Mentored by: Applied Materials
Mentors:
Data Science Bootcamp 2025 (Data)
Responsibilities:
Developing Noise Reduction algorithm and framework for evaluation.
Implemented classical denoising baselines (Gaussian, Bilateral, etc.) for benchmarking.
Trained U-Net–based deep learning models in Python/PyTorch on custom datasets.
Conducted architecture experiments and systematic hyperparameter tuning (loss functions, LR)
Built an evaluation framework using PSNR, SSIM, and custom metrics.
Developed a FastAPI + Docker backend with PostgreSQL/MinIO storage.
Created a PyQt desktop client for visual and metric-based comparison of all methods.
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Additional Projects
- Developed a full-stack scooter rental platform with location-based check-in/out using Google Maps integration and a complete booking workflow.
- Independently designed and developed a creative real-time “chaos” game, published online and gained strong player engagement.
3.
English Level
Fluent